Point-Based Surface Representation for Shape Similarity and Object Recognition

三维物体识别和曲面配准的一个关键问题是确定三维形状之间的相似度。虽然形状相似度量已经在二维形状的情况下提出，只有部分结果已经获得了三维形状。部分问题在于很难在3D表面上定义必要的与姿态无关的参考框架。本研究研究了3D形状的表示，可以直接从点集构建形状相似性的自然定义，而不需要特征提取、分割或表面拟合。这些表示可以调整为表面的全局表示（例如，用于对象识别）或更局部的表示（例如，用于跟踪局部表面补丁）。它们自然导致了直接适用于物体识别问题的3D形状匹配算法。除了将其应用于3D物体识别之外，这些表示将为3D形状分析提供一个框架。特别感兴趣的是形状类和通用对象识别的研究。通过实际应用的场景验证了形状表示和相关匹配算法。在一个应用中，物体识别用于建立和识别工业零件的几何模型。在第二个应用中，形状相似技术用于注册自然环境中地形和地标表面的视图，用于移动机器人导航和绘图。

A critical issue in recognizing 3D objects and in registering surfaces is the definition of a ound measure of similarity between 3D shapes. Although shape similarity metrics have been proposed in the case of 2D shapes, only partial results have been obtained for 3D shapes. Part of the problem is that it is difficult to define the necessary pose-independent reference frame on a 3D surface. This research investigates representations of 3D shapes that lead to a natural definition of shape similarity that can be built directly from point sets without requiring feature extraction, segmentation, or surface fitting. These representations may be adjusted to be either a global representation of a surface (e.g., for object recognition) or a more local representation (e.g., for tracking of local surface patches.) They lead naturally to 3D shape matching algorithms directly applicable to object recognition problems. Beyond their application to 3D object recognition, these representations will provide a framework for the analysis of 3D shapes. Of particular interest is the study of shape classes and generic object recognition. The shape representations and the associated matching algorithms are validated through scenarios motivated by real applications. In one application, object recognition is used for building and identifying geometric models of industrial parts. In a second application, the shape similarity technique is used for registering views of terrain and landmark surfaces in a natural environment for mobile robot navigation and mapping.